This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. In this project, we propose to extend viral infection models to include stochastic dynamics in order to study the onset events. The main novelty of the project is application of tools for deriving characteristics of stochastic (noisy) processes on networks, namely, the coarse graining technique and the analytic approach based on application of the so called Geometric Universality of Currents (GUC), which were recently developed by the PI. The first approach is the method for accelerated simulations of complex biological networks of reactions. The second approach is based on recent understanding that statistics of events in a large class of biological processes can be predicted analytically. This simplification follows from PI's discovery of GUC which can be applied to derive statistical characteristics of very complex processes. Such tools will likely be of use for understanding emerging experimental results, for vaccine development, and for other early intervention strategies.